Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Hortúa, Héctor J"'
Autor:
Ramos, Daniela L., Hortua, Hector J.
Colorectal polyps are generally benign alterations that, if not identified promptly and managed successfully, can progress to cancer and cause affectations on the colon mucosa, known as adenocarcinoma. Today advances in Deep Learning have demonstrate
Externí odkaz:
http://arxiv.org/abs/2407.16608
In recent years, science simulations have become popular among educators due to their educational usefulness, availability, and potential for increasing the students' knowledge on scientific topics. In this paper, we introduce the implementation of a
Externí odkaz:
http://arxiv.org/abs/2402.09866
Recently, deep learning techniques are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. In this paper, we leverage probabilistic deep learning for inferring the volatility index
Externí odkaz:
http://arxiv.org/abs/2401.17042
The new generation of galaxy surveys will provide unprecedented data allowing us to test gravity at cosmological scales. A robust cosmological analysis of the large-scale structure demands exploiting the nonlinear information encoded in the cosmic we
Externí odkaz:
http://arxiv.org/abs/2309.00612
There has been considerable interest in the community to understand if the Einstein and Jordan frames are either physically equivalent to each other or if there exists a preference frame where interpretations of physical observables should be done. I
Externí odkaz:
http://arxiv.org/abs/2303.01301
Publikováno v:
Front. Astron. Space Sci. Sec. Astrostatistics, Volume 10 - 2023
Methods based on Deep Learning have recently been applied on astrophysical parameter recovery thanks to their ability to capture information from complex data. One of these methods is the approximate Bayesian Neural Networks (BNNs) which have demonst
Externí odkaz:
http://arxiv.org/abs/2301.03991
Publikováno v:
In Fluid Phase Equilibria August 2024 583
Autor:
Hortua, Hector J.
In this paper, we use The Quijote simulations in order to extract the cosmological parameters through Bayesian Neural Networks. This kind of model has a remarkable ability to estimate the associated uncertainty, which is one of the ultimate goals in
Externí odkaz:
http://arxiv.org/abs/2112.11865
Markov Chain Monte Carlo (MCMC) algorithms are commonly used for their versatility in sampling from complicated probability distributions. However, as the dimension of the distribution gets larger, the computational costs for a satisfactory explorati
Externí odkaz:
http://arxiv.org/abs/2011.14276
Bayesian Neural Networks (BNNs) often result uncalibrated after training, usually tending towards overconfidence. Devising effective calibration methods with low impact in terms of computational complexity is thus of central interest. In this paper w
Externí odkaz:
http://arxiv.org/abs/2008.06729